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Gustavo E. A. P. A. Batista

Gustavo E. A. P. A. Batista

D-Index & Metrics

Computer Science

D-Index
40
Citations
14055
World Ranking
9043
National Ranking
278

Overview

Gustavo E. A. P. A. Batista is affiliated with the University of New South Wales in Australia. Their research primarily falls under the field of Computer Science, with a significant focus on several subfields including Artificial Intelligence, Computer Networks and Communications, Signal Processing, Electrical and Electronic Engineering, and Economics and Econometrics.

The scientist has explored a variety of topics within their work, such as:

  • Anomaly Detection Techniques and Applications
  • Network Security and Intrusion Detection
  • Stochastic Gradient Optimization Techniques
  • Internet Traffic Analysis and Secure E-voting
  • Data Stream Mining Techniques
  • Machine Learning and Data Classification
  • Age of Information Optimization

Gustavo E. A. P. A. Batista's recent publications demonstrate a diverse range of applied research areas:

  • COVID-Safe Spatial Occupancy Monitoring Using OFDM-Based Features and Passive WiFi Samples, 2021, ACM Transactions on Management Information Systems
  • Update Compression for Deep Neural Networks on the Edge, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • Pedestrian trajectory prediction using goal-driven and dynamics-based deep learning framework, 2025, Expert Systems with Applications
  • FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets, 2025, Proceedings of the ACM on Measurement and Analysis of Computing Systems
  • Efficient IoT Traffic Inference: From Multi-view Classification to Progressive Monitoring, 2023, ACM Transactions on Internet of Things

The scientist frequently collaborates with several other researchers, reflecting ongoing partnerships in their field. The most common co-authors include:

  • Hassan Habibi Gharakheili
  • Salil S. Kanhere
  • Zahra Donyavi
  • Yunrui Zhang
  • Arash Shaghaghi

Gustavo's work is published in various venues, with a concentration in a few prominent outlets. The most frequent publication venues include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • ACM Transactions on Management Information Systems
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • Expert Systems with Applications

Their research outputs cover a broad spectrum of computer science topics, often intersecting areas of artificial intelligence, data mining, and network security. Gustavo E. A. P. A. Batista's collaborations, in combination with the range of publication venues and topical diversity, illustrate an active engagement in advancing knowledge within these interconnected domains.

Best Publications

  • A study of the behavior of several methods for balancing machine learning training data

    Gustavo E. A. P. A. Batista;Ronaldo C. Prati;Maria Carolina Monard

  • Searching and mining trillions of time series subsequences under dynamic time warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • An analysis of four missing data treatment methods for supervised learning

    Gustavo E. A. P. A. Batista;Maria Carolina Monard

  • A Study of K-Nearest Neighbour as an Imputation Method.

    Gustavo E. A. P. A. Batista;Maria Carolina Monard

  • Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior

    Ronaldo C. Prati;Gustavo E. A. P. A. Batista;Maria Carolina Monard

  • A Complexity-Invariant Distance Measure for Time Series.

    Gustavo E. A. P. A. Batista;Xiaoyue Wang;Eamonn J. Keogh

  • CID: an efficient complexity-invariant distance for time series

    Gustavo E. Batista;Eamonn J. Keogh;Oben Moses Tataw;Vinícius M. Souza

  • Balancing Training Data for Automated Annotation of Keywords: a Case Study.

    Gustavo E. A. P. A. Batista;Ana L. C. Bazzan;Maria Carolina Monard

  • Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model

    Antonio Rafael Sabino Parmezan;Vinícius M. A Souza;Gustavo Enrique de Almeida Prado Alves Batista

  • Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • Challenges in benchmarking stream learning algorithms with real-world data

    Vinícius Mourão Alves de Souza;Vinícius Mourão Alves de Souza;Denis Moreira dos Reis;André Gustavo Maletzke;Gustavo Enrique de Almeida Prado Alves Batista;Gustavo Enrique de Almeida Prado Alves Batista

  • Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test

    Denis Moreira dos Reis;Peter Flach;Stan Matwin;Gustavo Batista

  • Class imbalance revisited: a new experimental setup to assess the performance of treatment methods

    Ronaldo C. Prati;Gustavo E. Batista;Diego F. Silva

  • Flying Insect Classification with Inexpensive Sensors

    Yanping Chen;Adena Why;Gustavo E. A. P. A. Batista;Agenor Mafra-Neto

  • Influence of graph construction on semi-supervised learning

    Celso André R. de Sousa;Solange O. Rezende;Gustavo E.A.P.A. Batista

  • Speeding up all-pairwise dynamic time warping matrix calculation

    Diego F. Silva;Gustavo Enrique de Almeida Prado Alves Batista

  • DTW-D: time series semi-supervised learning from a single example

    Yanping Chen;Bing Hu;Eamonn Keogh;Gustavo E.A.P.A Batista

  • A Survey on Graphical Methods for Classification Predictive Performance Evaluation

    R. C. Prati;G. E. A. P. A. Batista;M. C. Monard

  • Applying One-Sided Selection to Unbalanced Datasets

    Gustavo E. A. P. A. Batista;Andre C. P. L. F. Carvalho;Maria Carolina Monard

  • Time Series Classification Using Compression Distance of Recurrence Plots

    Diego F. Silva;Vinicius M. A. De Souza;Gustavo E. A. P. A. Batista

  • Learning with Skewed Class Distributions

    Maria Carolina Monard;Gustavo Enrique de Almeida Prado Alves Batista

  • Fast Unsupervised Online Drift Detection

    Denis Dos Reis;Gustavo Batista;Peter Flach;Stan Matwin

Frequent Co-Authors

Eamonn Keogh
Eamonn Keogh University of California, Riverside
André C. P. L. F. de Carvalho
André C. P. L. F. de Carvalho Universidade de São Paulo
Abdullah Mueen
Abdullah Mueen University of New Mexico
Daniel P. W. Ellis
Daniel P. W. Ellis Google (United States)
Ana L. C. Bazzan
Ana L. C. Bazzan Federal University of Rio Grande do Sul
Peter A. Flach
Peter A. Flach University of Bristol
Aruna Seneviratne
Aruna Seneviratne University of New South Wales
João Gama
João Gama University of Porto
Stan Matwin
Stan Matwin Dalhousie University

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